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library(ggplot2)
library(plotly)

package 㤼㸱plotly㤼㸲 was built under R version 4.0.4Registered S3 method overwritten by 'data.table':
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  print.data.table     
Registered S3 methods overwritten by 'htmltools':
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  print.html           tools:rstudio
  print.shiny.tag      tools:rstudio
  print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'htmlwidgets':
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  print.htmlwidget tools:rstudio

Attaching package: 㤼㸱plotly㤼㸲

The following object is masked from 㤼㸱package:ggplot2㤼㸲:

    last_plot

The following object is masked from 㤼㸱package:stats㤼㸲:

    filter

The following object is masked from 㤼㸱package:graphics㤼㸲:

    layout

library(shiny)

package 㤼㸱shiny㤼㸲 was built under R version 4.0.4

library(dplyr)

Attaching package: 㤼㸱dplyr㤼㸲

The following objects are masked from 㤼㸱package:stats㤼㸲:

    filter, lag

The following objects are masked from 㤼㸱package:base㤼㸲:

    intersect, setdiff, setequal, union

source("./DataProccess.R")

Registered S3 methods overwritten by 'dbplyr':
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  print.tbl_sql      
-- Attaching packages ------------------------------------------------------------------------------------------------------------- tidyverse 1.3.0 --
v tibble  3.0.5     v purrr   0.3.4
v tidyr   1.1.2     v stringr 1.4.0
v readr   1.4.0     v forcats 0.5.0
-- Conflicts ---------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks plotly::filter(), stats::filter()
x dplyr::lag()    masks stats::lag()

Attaching package: 㤼㸱lubridate㤼㸲

The following objects are masked from 㤼㸱package:base㤼㸲:

    date, intersect, setdiff, union


Attaching package: 㤼㸱zoo㤼㸲

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HFGData="../data/HFG data for stats preliminary 3-18-21.xlsx"

HFGFrame=HFGInfo(HFGData)%>%
  mutate("Filter Rep"=as.character(`Filter Rep`),`Well Rep`=as.character(`Well Rep`))

New names:
* `` -> ...2

HFGFrame.mean=HFGFrame%>%
  ungroup()%>%
  group_by(Date,`Filter Rep`,Plant)%>%
  summarise(N1GC=mean(N1GC))%>%
  ungroup()%>%
  filter(N1GC<10000000)

`summarise()` has grouped output by 'Date', 'Filter Rep'. You can override using the `.groups` argument.

HFGFrame.Filtered=HFGFrame%>%
  filter(N1GC<100000000)%>%
  filter(!is.na(N1GC),!is.na(Date))


HFGDFrame=HFGFrame.Filtered

p=ggplot()+
geom_jitter(data=HFGDFrame,aes(x=Date, y=N1GC , color=`Filter Rep`,text=Plant),size=1, position = position_jitter(width = .1, height = 0))+
  geom_smooth(data=HFGFrame.mean,aes(x=Date,y=N1GC, color=`Filter Rep`),se=F)

Ignoring unknown aesthetics: text

ggplotly(p,tooltip = c("text", "size"))

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Jitter_POSIXlt = function (x, factor = 1, amount = 86400/4) 
{
    if (length(x) == 0L) 
        return(x)
    if (!is.POSIXlt(x)) 
        stop("'x' must be Date")
    x + stats::runif(length(x), -amount, amount)
}




#Data needs to be sorted by category being filtered
HFGDFrame2 <- HFGDFrame[order(HFGDFrame$`Filter Rep`), ]

Plants=unique(HFGDFrame$Plant)

button = list()
button[[1]] = list(method = "restyle",
               args = list("transforms[0].value", unique(HFGDFrame$Plant)),
               label = unique("All"))


for (i in 2:length(Plants)){
  button[[i]]=list(method = "restyle",
               args = list("transforms[0].value", unique(HFGDFrame$Plant)[i]),
               label = unique(HFGDFrame$Plant)[i])
}
fig <-HFGDFrame%>%
  plot_ly(
  x = Jitter_POSIXlt(as.POSIXlt(HFGDFrame$Date)),
  text = ~`Plant`,
  hoverinfo = 'text',
  transforms = list(
      list(
        type = 'filter',
        target = HFGDFrame2$Plant,
        operation = 'in',
        value = unique(HFGDFrame$Plant)[1]
      )
    )
)%>%
   add_trace(type = 'scatter',
              y = ~N1GC,
             color = ~`Filter Rep`,
             mode = 'markers') %>%
  add_lines(y = fitted(loess(N1GC ~ as.numeric(Date),data=HFGDFrame)),
            line = list(color = '#07A4B5'))%>%
  layout(
    updatemenus = list(
      list(
        type = 'dropdown',
        active = 0,
        buttons = button
      )
    )
  )
fig

#1)Best version of modeling
#2)Same scale-log Scale-some way to see outlines-once we see it in N1-look at N2 instead- CT variance-calibrate on one then shifts to other.
#3)graph variance per well
#L)N1 should not change much day to day-if it does->some issue with measurement->understand measurement to get good smooth->toss minority examples
#group by wells->summarize->group by filter->summarize->have estimate for day.
#or interquartile to get reasonable values->mean of those->mean W/O outlines
#compare change over time
#median of the replicates might be very useful
#one picture to justify medians
#median lines to show the rep are close-exuding outlines
#rank testing -rank them in order-to see if they are close
#multi-layered
#rep from filter-same distribution-rank all 9 compare largest and smallest
#LOOK AT PREVIOUS WORK

#Give them a things to look at
#prep them to get a good understanding
#Fix axis log scale
#lets them pick filter
#day to day variance

#Set day to work to work on repo
#friday?
ui <- fluidPage(
  titlePanel("Dashboard"),
  
  sidebarLayout(
    sidebarPanel(
      selectInput(inputId = "Plant", label = ("Plant"),
                  choices = unique(HFGDFrame$Plant),
                  multiple=T 
      ),
      selectInput(inputId = "Line", label = ("Line Method"),
                  choices = c("None","Loess","mean value","Linear Model")
      )
    ),
    mainPanel(plotOutput("plot2"))
  )
)

                                        
server <- function(input, output) {
  filtered_data<- reactive({
    filter(HFGDFrame, HFGDFrame$Plant %in%  input$Plant)})
  Filtered_mean = reactive({
    filtered_data()%>%
      group_by(Date,`Filter Rep`)%>%
      summarize(N1GC=mean(N1GC))
  })
  output$plot2<-renderPlot({
    ggplot()+aes(x=Date,color=`Filter Rep`)+
      geom_jitter(data=filtered_data(),aes(y=N1GC,shape=Plant,fill=`Filter Rep`),height=0,width=.05)+
      scale_shape_manual(values=1:length(unique(HFGDFrame$Plant)))+
      geom_line(data=Filtered_mean(),aes(y=N1GC+ifelse(input$Line=="mean value",0,NA)),size=1)+
      geom_smooth(data=filtered_data(),aes(y=N1GC+ifelse(input$Line=="Loess",0,NA)),se=F)+
      geom_smooth(data=filtered_data(),method='lm',aes(y=N1GC+ifelse(input$Line=="Linear Model",0,NA)),se=F)+
      scale_y_log10(limits = c(min(HFGDFrame$N1GC),max(HFGDFrame$N1GC)))
      },height = 600,width = 800)
}

shinyApp(ui = ui, server = server)

Listening on http://127.0.0.1:7748

NA

Waterfilename <- "../data/WW SARS-COV-2 Data V5.xlsx"
CovidFileName = "../data/MMSD_Cases.csv"
source("./DataProccess.R")

Registered S3 methods overwritten by 'dbplyr':
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  print.tbl_lazy     
  print.tbl_sql      
-- Attaching packages ------------------------------------------------------------------------------------------------------------- tidyverse 1.3.0 --
v ggplot2 3.3.3     v purrr   0.3.4
v tibble  3.0.5     v dplyr   1.0.3
v tidyr   1.1.2     v stringr 1.4.0
v readr   1.4.0     v forcats 0.5.0
-- Conflicts ---------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

Attaching package: 㤼㸱lubridate㤼㸲

The following objects are masked from 㤼㸱package:base㤼㸲:

    date, intersect, setdiff, union


Attaching package: 㤼㸱zoo㤼㸲

The following objects are masked from 㤼㸱package:base㤼㸲:

    as.Date, as.Date.numeric

Registered S3 method overwritten by 'data.table':
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  print.data.table     

Restarting R session...